Chapter 5 Results

5.1 US time series

This graph shows the time series of confirmed cases growth and people vaccinated growth. It’s obvious that when people vaccinated growth number is high, confirmed cases growth speed decreases and when people vaccinated low, confirmed cases increases again. This plot confirms that vaccination helps to prevent covid spread.

5.2 US cumulative cases

This is a map that shows cumulative confirmed cases in United States on 12/9/2021. Red points mean confirmed cases and the size represents exact number. The higher the number, the larger the spot. We can see that large cities are serious spots. This graph gives us a general view of current covid situation.

5.3 Latest cummulative cases and deaths

The graphs above shows current confirmed cases and death of 50 states. We can see that California, Texas, Florida, New York, and Illinois are top 5 states with most confirmed cases. California, Texas, Florida, New York, and Pennsylvania are top 5 states with most deaths.

5.4 Latest vaccination situation

As there are 12 features in our vaccination data, we use a correlation heatmap to select the most relevant features to confirmed cases and deaths. From the graph above we can see the top 4 features are total vaccinations, total distributed, people vaccinated, and people fully vaccinated. We will use people vaccinated and people fully vaccinated as two features to represent vaccination.

The 5 states with most deaths also have the largest number of people vaccinated. However, none of them are on top of the list of people fully vaccinated.

5.5 Time series of serious states

There is a significant outlier in original people_vaccinated daily growth graph. Because from October 2nd to November 28th, the people_vaccinated information in Pennsylvania is missing. We are doing a filling by pervious data, therefore there exists a huge pike. We have to delete the data of that day.

5.6 Distribution

This scatterplot shows cumulative people fully vaccinated vs. cases in all states. We can easily see the outliers among 50 states. If we see in ratio, there’s no significantly high or low states.

5.7 Percentage of number of beds in population VS death rate

According to the graph, there is no clear trend between the percentage of beds inn population and the death rate. Especially when we ignore the state which has more than 0.035. It’s different from what we initially imagined, when capacity of hospital in a state increase, the mortality rate will drop. It may cause by several reasons. The first reason may be due to inaccurate mortality. Because the symptoms of COVID-19 are similar to those of flu, many people will mistakenly think that they are flu instead of COVID-19, leading to deviations in statistical data. The second possibility is that there are other factors, such as the number of ventilators or whether the isolation measures are effective. These are also factors that can affect mortality.

5.8 inpatient rate and death rate

Form the above graph, we can see that death rate increase as the inpatient rate increases. There are two possible explanations. The first one is people would not go to hospital unless they are in very bad situation. Thus as the number of impatient increase, death rate increase. The second explanation is consider about the time range. There is no very useful way to cure COVID-19 patient. Thus as inpatient number increase, death rate increase.